Grounded in Shannon’s seminal work, information theory quantifies the uncertainty—or entropy—in data sources and sets fundamental limits on how efficiently information can be represented.

Information theory provides the fundamental framework for understanding and designing data compression algorithms. At its core lies the concept of entropy, a quantitative measure that reflects the ...

Week 1: Introduction to information theory and coding: probabilistic information measure and entropy Weeks 2-3: Source coding and source coding theorem, prefix coding and Huffman codes, mutual ...

In information theory, such tools have been used to characterize and gain new insights into the capacity (i.e., the maximum reliable rate) of multi-user networks. Tutorial Papers Dytso, A.; Bustin, R.

Entropy and information are both emerging as currencies of interdisciplinary dialogue, most recently in evolutionary theory. If this dialogue is to be fruitful, there must be general agreement about ...

We describe and develop a close relationship between two problems that have customarily been regarded as distinct: that of maximizing entropy, and that of minimizing worst-case expected loss. Using a ...

Previous research has shown that black holes have entropy and emit radiation, phenomena that hint at deeper links between information theory and spacetime geometry.